结合图元与感知哈希的手写输入简笔画识别

目的 为了克服手写输入中随意性强和自由度大的缺陷,同时兼顾简笔画的整体属性和局部特征,提出一种基于图元识别与感知哈希技术相结合的手写输入简笔画二级识别算法。方法 首先提取笔画的几何特征、笔序特征及结构特征且进行识别,然后查找由图元信息、笔画结构信息和笔序信息构成的简笔画语义库,完成由规则的几何图元构成的简笔画识别;若未被识别,则生成简笔画图像,利用感知哈希技术完成简笔画图像的识别。结果 基于本文提出的简笔画识别方法,实现了对样本库中150种简笔画对象的识别,平均识别率为82.6%。结论 实验结果表明,对于不同用户手写输入的任意样本库中的简笔画,该方法具有较高的识别率,此外,还可以通过在简笔画语义库和样本库中增加简笔画的种类等方式实现对更多种类简笔画的扩展识别。

Handwritten sketch recognition based on sketch entity and perceptual hashing

Objective A two-stage identification method based on sketch entity identification and the perceptual hashing technique is proposed to overcome the defect of strong randomness and excessive freedom in handwritten sketches and balance the overall properties and local characteristics of a sketch. Method First, the geometrical characteristics of the stroke, the stroke order, and the stroke structural characteristics of the input handwritten sketch are extracted. Second, a semantic library of sketches containing information on entity, stroke structure, and stroke order is searched to recognize a sketch composed of a regular geometric entity. If no proper sketch is available in the library, an image of asketch is generated and recognition of the sketch image is implemented with perceptual hashing technology. Result With the sketch recognition method proposed in this paper, recognition of 150 types of sketches in a database was achieved; the average recognition rate is 82.6%. Conclusion Experimental results show that the proposed method has a high recognition rate for any handwritten sketch of database input by different users. The method also allows for extensive identification of other sketches by adding sketch types to the semantic library of sketches and the database.